Hi,
I am working on a classification problem where I have 3 classes [good (180), mediocre (4535), bad (183)]. (#number of examples in that class)
In my rapidminer process I only learn a model for "good" and "bad" and in the testing phase I want to modify the prediction depending on the confidence of my classifier. So I am filtering out all examples with low confidence and assign them to the "default class" "mediocre".
In order to do this reassignment I use a "filter example" operator together with a "replace" operator.
My problem is:
If I run my process without my reassignment step (i.e. filtering and replacing) I get the expected values for true good (180), true mediocre(4535) and true bad (183) in my confusion matrix. However, if I do the reassignment my confusion matrix yields unexpected values for true good, mediocre and bad.
Why is that happening?
My process as follows:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.015">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve DataSet-WhiteWine" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Local Repository/data/GroupProject_WineQuality_White"/>
</operator>
<operator activated="true" class="set_role" compatibility="5.3.015" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
<parameter key="attribute_name" value="quality"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="generate_id" compatibility="5.3.015" expanded="true" height="76" name="Generate ID" width="90" x="313" y="30">
<parameter key="create_nominal_ids" value="false"/>
<parameter key="offset" value="0"/>
</operator>
<operator activated="true" class="normalize" compatibility="5.3.015" expanded="true" height="94" name="Normalize" width="90" x="447" y="30">
<parameter key="return_preprocessing_model" value="false"/>
<parameter key="create_view" value="false"/>
<parameter key="attribute_filter_type" value="all"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="numeric"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="real"/>
<parameter key="block_type" value="value_series"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_series_end"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="method" value="range transformation"/>
<parameter key="min" value="0.0"/>
<parameter key="max" value="1.0"/>
</operator>
<operator activated="true" class="discretize_by_user_specification" compatibility="5.3.015" expanded="true" height="94" name="Discretize" width="90" x="581" y="30">
<parameter key="return_preprocessing_model" value="false"/>
<parameter key="create_view" value="false"/>
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value="quality"/>
<parameter key="attributes" value="quality"/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="numeric"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="real"/>
<parameter key="block_type" value="value_series"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_series_end"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="attribute_type" value="nominal"/>
<list key="classes">
<parameter key="bad" value="4.0"/>
<parameter key="mediocre" value="7.0"/>
<parameter key="good" value="10.0"/>
</list>
</operator>
<operator activated="true" class="x_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="715" y="30">
<parameter key="create_complete_model" value="false"/>
<parameter key="average_performances_only" value="true"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_validations" value="30"/>
<parameter key="sampling_type" value="shuffled sampling"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1985"/>
<process expanded="true">
<operator activated="true" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples" width="90" x="45" y="30">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="quality != mediocre"/>
<parameter key="invert_filter" value="false"/>
</operator>
<operator activated="true" class="naive_bayes" compatibility="5.3.015" expanded="true" height="76" name="Naive Bayes" width="90" x="179" y="30">
<parameter key="laplace_correction" value="true"/>
</operator>
<connect from_port="training" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Naive Bayes" to_port="training set"/>
<connect from_op="Naive Bayes" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.3.015" expanded="true" height="76" name="Multiply" width="90" x="179" y="30"/>
<operator activated="true" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples (3)" width="90" x="112" y="210">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="confidence(bad)<0.999 && confidence(good)<0.99"/>
<parameter key="invert_filter" value="false"/>
</operator>
<operator activated="true" breakpoints="after" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples (2)" width="90" x="246" y="255">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="confidence(bad)<0.999 && confidence(good)<0.99"/>
<parameter key="invert_filter" value="true"/>
</operator>
<operator activated="true" breakpoints="after" class="replace" compatibility="5.3.015" expanded="true" height="76" name="Replace (3)" width="90" x="246" y="165">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="prediction(quality)"/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="file_path"/>
<parameter key="block_type" value="single_value"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="single_value"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="replace_what" value="bad|good"/>
<parameter key="replace_by" value="mediocre"/>
</operator>
<operator activated="true" class="union" compatibility="5.3.015" expanded="true" height="76" name="Union" width="90" x="447" y="210"/>
<operator activated="true" class="select_attributes" compatibility="5.3.015" expanded="true" height="76" name="Select Attributes" width="90" x="380" y="30">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="|quality|prediction(quality)"/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="514" y="30">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="false"/>
<parameter key="kappa" value="false"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Filter Examples (3)" to_port="example set input"/>
<connect from_op="Filter Examples (3)" from_port="example set output" to_op="Replace (3)" to_port="example set input"/>
<connect from_op="Filter Examples (3)" from_port="original" to_op="Filter Examples (2)" to_port="example set input"/>
<connect from_op="Filter Examples (2)" from_port="example set output" to_op="Union" to_port="example set 2"/>
<connect from_op="Replace (3)" from_port="example set output" to_op="Union" to_port="example set 1"/>
<connect from_op="Union" from_port="union" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve DataSet-WhiteWine" from_port="output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Generate ID" to_port="example set input"/>
<connect from_op="Generate ID" from_port="example set output" to_op="Normalize" to_port="example set input"/>
<connect from_op="Normalize" from_port="example set output" to_op="Discretize" to_port="example set input"/>
<connect from_op="Discretize" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
Through a bit of debugging the operators I found out that if you just add an "Append" operator with only one input (the actual output of "apply model" nothing else) in the testing phase of X-Validation the confusion matrix yields wrong values for true <classname>.
In the above process I first used "Append" and then changed it to the "Union" operator, however I am still having the same problem.
Am I doing anything wrong?
Thanks in advance for your help!!!
